Graph kernel based link prediction for signed social networks

By revealing potential relationships between users, link prediction has long been considered as a fundamental research issue in singed social networks. The key of link prediction is to measure the similarity between users. Existing works use connections between target users or their common neighbors to measure user similarity. Rich information available for link prediction is missing since use similarity is widely influenced by many users via social connections. We therefore propose a novel graph kernel based link prediction method, which predicts links by comparing user similarity via signed social network’s structural information: we first generate a set of subgraphs with different strength of social relations for each user, then calculate the graph kernel similarities between subgraphs, in which Bhattacharyya kernel is used to measure the similarity of the k-dimensional Gaussian distributions related to each k-order Krylov subspace generated for each subgraph, and finally train SVM classifier with user similarity information to predict links. Experiments held on real application datasets show that our proposed method has good link prediction performances on both positive and negative link prediction. Our method has significantly higher link prediction accuracy and F1-score than existing works.     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

基于社交圈层和注意力机制的信息热度预测

社交网络现已成为现实世界中信息传播与扩散的主要媒介,对其中的热点信息进行建模和预测有着广泛的应用场景和商业价值,比如进行信息传播挖掘、广告推荐和用户行为分析等.目前的相关研究主要利用特征和时间序列进行建模,但是并没有考虑到社交网络中用户的社交圈层对于信息传播的作用.本文提出了一种基于社交圈层和注意力机制的热度预测模型S...     

基于文本引导的注意力图像转发预测排序网络

转发预测在社交媒体网站(Social media sites, SMS)中是一个很有挑战性的问题. 本文研究了SMS中的图像转发预测问题, 预测用户再次转发图像推特的图像共享行为. 与现有的研究不同, 本文首先提出异构图像转发建模网络(Image retweet modeling, IRM), 所利用的是用户之前转发图像推特中的相关内容、之后在SMS中的联系和被转发者的偏好三方面的内容. 在此基础上, 提出文本引导的多模态神经网络, 构建新型多方面注意力排序网络学习框架, 从而学习预测任务中的联合图像推特表征和用户偏好表征. 在Twitter的大规模数据集上进行的大量实验表明, 我们的方法较之现有的解决方案而言取得了更好的效果.     

Finding Diachronic Like‐Minded Users

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.     

Models of Influence in Online Social Networks

Online social networks gained their popularity from relationships users can build with each other. These social ties play an important role in asserting users' behaviors in a social network. For example, a user might purchase a product that his friend recently bought. Such phenomenon is called social influence, which is used to study users' behavior when the action of one user can affect the behavior of his neighbors in a social network. Social influence is increasingly investigated nowadays as it can help spreading messages widely, particularly in the context of marketing, to rapidly promote products and services based on social friends' behavior in the network. This wide interest in social influence raises the need to develop models to evaluate the rate of social influence. In this paper, we discuss metrics used to measure influence probabilities. Then, we reveal means to maximize social influence by identifying and using the most influential users in a social network. Along with these contributions, we also survey existing social influence models, and classify them into an original categorization framework. Then, based on our proposed metrics, we show the results of an experimental evaluation to compare the influence power of some of the surveyed salient models used to maximize social influence.     

Protect You More Than Blank: Anti-Learning Sensitive User Information in the Social Networks

Social networks are getting more and more attention in recent years. People join social networks to share their information with others. However, due to the different cultures and backgrounds, people have different requirements on what kind of information should be published. Currently, when social network websites publish data, they just leave the information that a user feels sensitive blank. This is not enough due to the existence of the label-structure relationship. A group of analyzing algorithms can be used to learn the blank information with high accuracy. In this paper, we propose a personalized model to protect private information in social networks. Specifically, we break the label-structure association by slightly changing the edges in some users’ neighborhoods. More importantly, in order to increase the usability of the published graph, we also preserve the influence value of each user during the privacy protection. We verify the effectiveness of our methods through extensive experiments. The results show that the proposed methods can protect sensitive labels against learning algorithms and at the same time, preserve certain graph utilities.     

Improving music recommendation by incorporating social influence

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

     

基于领域的影响力最大化与病毒营销

近年来随着新浪微博、人人网等社交网络新媒体的涌现,线上影响力传播得到了越来越多企业和研究机构的关注。如何在给定资源的约束下实现最大的传播范围(影响力最大化问题),对病毒营销等市场战略的有效开展有着重要意义。如果能充分利用社交网络上的异质性信息来更准确地定位用户所属的领域,进而基于领域实现影响力最大化,将对从整体角度出发的传统研究和片面的结构或内容角度的研究形成很好的补充。该文同时利用新浪微博上用户之间的社交关系和微博内容的话题两个维度的信息将用户划分为不同的领域;进而提出了一种基于贪心和动态规划混合的改良算法实现基于领域的影响力最大化。实验表明该文的领域影响力模型较好优化了传统影响力最大化的时间消耗,同时拥有相近的精度。     

Understanding the mechanism of social tie in the propagation process of social network with communication channel

The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers have proposed a series of propagation models, generally, which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works, so that the existing models failed to clearly describe the process a user be activated by a neighbor. To this end, in this paper, we analyze the close correspondence between social tie in propagation process and communication channel, thus we propose to exploit the communication channel to describe the information propagation process between users, and design a social tie channel (STC) model. The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference, and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.     

一种面向获取空间信息的潜在好友推荐算法

随着社交网络的不断发展,朋友推荐已成为各大社交网络的青睐对象,在能够帮助用户拓宽社交圈的同时可以通过新朋友获取大量信息.由此朋友推荐应该着眼于拓宽社交圈和获取信息,然而传统的朋友推荐算法几乎没有考虑从获取信息的角度为用户推荐潜在好友,大多是依赖于用户在线的个人资料和共同的物理空间中的签到信息.而由于人们活动具有空间局部性,被推荐的好友分布在用户了解的地理空间,并不能够满足用户通过推荐的朋友获取更多地理信息的需求.本文采用用户在物理世界中的签到行为代替虚拟社交网络中的用户资料,挖掘真实世界中用户之间的签到行为的相似性,为用户推荐具有相似的签到行为且地理位置分布更广泛的陌生人,能够增加用户接受被推荐的陌生人成为朋友的可能性和在保证一定的推荐精度的基础上增加用户的信息获取量.本文采用核密度估计估算用户签到行为概率分布,用时间熵度量签到行为在时间上的集中程度,选择可以为用户带来更多新的地理信息的陌生人作为推荐的对象,通过大规模Foursquare的用户签到数据集验证本文的算法在精度上保证了与目前已有LBSN上陌生人推荐算法的相似性,在信息扩大程度上高于上述已有算法.     

Users joining multiple sites: Friendship and popularity variations across sites

Our social media experience is no longer limited to a single site. We use different social media sites for different purposes and our information on each site is often partial. By collecting complementary information for the same individual across sites, one can better profile users. These profiles can help improve online services such as advertising or recommendation across sites. To combine complementary information across sites, it is critical to understand how information for the same individual varies across sites. In this study, we aim to understand how two fundamental properties of users vary across social media sites. First, we study how user friendship behavior varies across sites. Our findings show how friend distributions for individuals change as they join new sites. Next, we analyze how user popularity changes across sites as individuals join different sites. We evaluate our findings and demonstrate how our findings can be employed to predict how popular users are likely to be on new sites they join.     

Relationship Identification Across Heterogeneous Online Social Networks

In the era of the social web, many people manage their social relationships through various online social networking services. It has been found that identifying the types of social relationships among users in online social networks facilitates the marketing of products via electronic “word of mouth.” However, it is a great challenge to identify the types of social relationships, given very limited information in a social network. In this article, we study how to identify the types of relationships across multiple heterogeneous social networks and examine if combining certain information from different social networks can help improve the identification accuracy. The main contribution of our research is that we develop a novel decision tree initiated random walk model, which takes into account both global network structure and local user behavior to bootstrap the performance of relationship identification. Experiments conducted based on two real‐world social networks, Sina Weibo and Jiepang, demonstrate that the proposed model achieves an average accuracy of 92.0%, significantly outperforming other baseline methods. Our experiments also confirm the effectiveness of combining information from multiple social networks. Moreover, our results reveal that human mobility features indicating location categories, coincidence, and check‐in patterns are among the most discriminative features for relationship identification.     

基于桥梁用户的多社交网络影响最大化

单社交网络影响最大化问题已经得到了学术界的广泛关注与研究,然而如今多社交网络之间呈现信息互通的趋势.多社交网络中存在的桥梁用户（Bridge User,BU）（即同时拥有多个社交网络账户的用户）,可将信息从一个社交网络分享至另外一个社交网络,信息传播不再局限于单个网络.本文针对多社交网络信息影响最大化进行了相关研究,分析了桥梁用户在多社交网络信息传播中的作用,提出了基于桥梁用户的多社交网络聚合算法,并在得到的聚合图上对多社交网络影响最大化问题进行求解.仿真实验对多社交网络影响最大化问题进行了求解,并证实了桥梁用户在多社交网络信息传播时的作用.     

An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions

在社会网络的影响的测量在数据采矿社区收到了很多注意。影响最大化指发现尽量利用信息或产品采纳的有影响的用户的过程。在真实设置,在一个社会网络的一个用户的影响能被行动的集合建模(例如,份额,重新鸣叫,注释) 在其出版物以后由网络的另外的用户表现了。就我们的知识而言,在文学的所有建议模型同等地对待这些行动。然而,它是明显的一工具少些比一样的出版的份额影响的一份出版物相似。这建议每个行动有它影响的自己的水平(或重要性) 。在这份报纸,我们建议一个模型(叫的社会基于行动的影响最大化模型, SAIM ) 为在社会网络的影响最大化。在 SAIM,行动没在测量一个个人的影响力量同等地被考虑,并且它由二主要的步组成。在第一步,我们在社会网络计算每个个人的影响力量。这影响力量用 PageRank 从用户行动被计算。在这步的结束,我们得到每个节点被它的影响力量在标记的一个加权的社会网络。在 SAIM 的第二步,我们计算一个新概念说出 influence-BFS 树的使用的有影响的节点的一个最佳的集合。在大规模真实世界、合成的社会网络上进行的实验在计算揭示我们的模型 SAIM 的好表演,在可接受的时间规模,允许信息的最大的传播的有影响的节点的一个最小的集合。     

Flickr group recommendation with auxiliary information in heterogeneous information networks

Over the past few years, the appropriate utilization of user communities or image groups in social networks (i.e., Flickr or Facebook) has drawn a great deal of attention. In this paper, we are particularly interested in recommending preferred groups to users who may favor according to auxiliary information. In real world, the images captured by mobile equipments explicitly record a lot of contextual information (e.g., locations) about users generating images. Meanwhile, several words are employed to describe the particular theme of each group (e.g., “Dogs for Fun Photos” image group in Flickr), and the words may mention particular entities as well as their belonging categories (e.g., “Animal”). In fact, the group recommendation can be conducted in heterogeneous information networks, where informative cues are in general multi-typed. Motivated by the assumption that the auxiliary information (visual features of images, mobile contextual information and entity-category information of groups in this paper) in heterogeneous information networks will boost the performance of the group recommendation, this paper proposes to combine auxiliary information with implicit user feedback for group recommendation. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user–user similarity via visual features and heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation approaches.     

基于最短时间距离的校园无线网络用户关联性度量

在校园网络中,存在着大量的信息系统,记录着用户的日常行为信息。通过对大量用户的日常轨迹信息分析,可以发现用户之间的行为关联性,度量用户之间的社会关系强度。基于上海某校的校园网络数据特点,提出了一种改进的基于用户时间序列模型,用最短时间距离进行社会关系度量的方法。该方法首先依据用户的行为数据生成用户行为时间序列,并在此基础上进行行为关联性的度量,以反映用户在真实世界中的社会关系强度,并利用地点访问热度修正社会关系强度的分析结果。实验中使用该方法对上海某校的校园网数据进行分析,度量用户关联性强度,验证了该方法的有效性。     

Context-aware QoS prediction for web service recommendation and selection

QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.     

Incentive Cooperation Strategies for Peer-to-Peer Live Multimedia Streaming Social Networks

Multimedia social networks have become an emerging research area, in which analysis and modeling of the behavior of users who share multimedia are of ample importance in understanding the impact of human dynamics on multimedia systems. In peer-to-peer live-streaming social networks, users cooperate with each other to provide a distributed, highly scalable and robust platform for live streaming applications. However, every user wishes to use as much bandwidth as possible to receive a high-quality video, while full cooperation cannot be guaranteed. This paper proposes a game-theoretic framework to model user behavior and designs incentive-based strategies to stimulate user cooperation in peer-to-peer live streaming. We first analyze the Nash equilibrium and the Pareto optimality of two-person game and then extend to multiuser case. We also take into consideration selfish users' cheating behavior and malicious users' attacking behavior. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free, attack resistance and help to provide reliable services.